Systems and methods for generating gratuity analytics for one or more restaurants

ABSTRACT

A gratuity analytics computing device for generating gratuity analytics for one or more restaurants is provided. The gratuity analytics computing device includes a memory in communication with a processor. The processor programmed to receive a date range from a client computing device. The processor is further configured to receive transaction data for transactions occurring within the date range at a restaurant. The transaction data including a manager identifier, a time stamp, and an employee identifier associated with the transactions, the transaction data including authorization messages and clearing messages. The processor is further configured to match a plurality of authorization messages with a respective plurality of clearing messages. The processor is further configured to calculate tip data for the restaurant based on the plurality of matched messages, generate gratuity analytics for the restaurant over the date range based on the tip data, and display on a user interface of the client computing device the gratuity analytics.

BACKGROUND

The present application relates generally to a technology that may beused to assist in predicting merchant performance, and moreparticularly, to network-based systems and methods for analyzing andcomparing businesses based on gratuity information associated with thosebusinesses.

A gratuity, also referred to as a tip, is an amount of money tendered bya customer to a person associated with a merchant (e.g., an employee) asa way of thanking the person for a service performed. The tip is anamount of money typically paid by the customer in addition to the baseprice of the service or the goods purchased by the customer. Tipping isa widely practiced social custom in the United States and is usuallyvoluntary. For example, tips are generally given for services atrestaurants, golf courses, hotels, spas and salons, casinos, barbershops, as well as to taxi drivers, movers, and the like. Higher tips maybe given for excellent service while smaller tips or no tips may begiven for poor service. In the United States, certain types ofemployees, such as waiters and waitresses in a restaurant, may relymostly on tips for income because their employers are allowed to paythem less than minimum wage with the expectation that the employee'sincome will be supplemented by tips.

Tipping may also provide insight into whether a community is flourishingor dwindling financially. For example, if an average tipping amount in aparticular area is trending upward, it may be reasonable to assume thatthe community in that area is likewise trending upward financially.Tipping may also provide insight into the performance of a restaurant,group of restaurants in a geographic area, manager, and/or employee.

BRIEF DESCRIPTION

In one aspect, a gratuity analytics computing device for generatinggratuity analytics for one or more restaurants, said gratuity analyticscomputing device comprising a memory in communication with a processoris provided. The processor is programmed to receive a date range from aclient computing device, and receive transaction data for transactionsoccurring within the date range at a restaurant, wherein the transactiondata includes a manager identifier, a time stamp, and an employeeidentifier associated with the transactions, and wherein thetransactions include authorization messages and clearing messages. Theprocessor is further programmed to match a plurality of authorizationmessages with a respective plurality of clearing messages, and calculatetip data for the restaurant based on the plurality of matched messages.The processor is further programmed to generate gratuity analytics forthe restaurant over the date range based on the tip data, and display ona user interface of the client computing device the gratuity analytics.

In another aspect, a method for generating gratuity analytics for one ormore restaurants is provided. The method is implemented by a gratuityanalytics computing device having at least one processor incommunication with a memory. The gratuity analytics computing device isin communication with a client computing device. The method includesreceiving a date range from a client computing device, and receivingtransaction data for transactions occurring within the date range at arestaurant, wherein the transaction data includes a manager identifier,a time stamp, and an employee identifier associated with thetransactions, and wherein the transactions include authorizationmessages and clearing messages. The method further includes matching aplurality of authorization messages with a respective plurality ofclearing messages, and calculating tip data for the restaurant based onthe plurality of matched messages. The method further includesgenerating gratuity analytics for the restaurant over the date rangebased on the tip data, and displaying on a user interface of the clientcomputing device the gratuity analytics.

In a further aspect, a computer-readable storage medium havingcomputer-executable instructions embodied thereon is provided. Whenexecuted by a gratuity analytics computing device including at least oneprocessor in communication with a memory, the computer-readableinstructions cause the gratuity analytics computing device to receive adate range from a client computing device, and receive transaction datafor transactions occurring within the date range at a restaurant,wherein the transaction data includes a manager identifier, a timestamp, and an employee identifier associated with the transactions, andwherein the transaction data includes authorization messages andclearing messages. The computer-readable instructions further cause thecomputing device to match a plurality of authorization messages with arespective plurality of clearing messages, calculate tip data for therestaurant based on the plurality of matched messages, generate gratuityanalytics for the restaurant over the date range based on the tip data,and display on a user interface of the client computing device thegratuity analytics.

In a further aspect, a gratuity analytics computing device forgenerating real-time gratuity analytics for one or more restaurants isprovided. The gratuity analytics computing device includes a memory incommunication with a processor. The processor is programmed to receive areal-time point-of-sale (POS) data feed from a POS device associatedwith a restaurant. The processor is further programmed to receive, aspart of the POS data feed, an authorization transaction initiated by acardholder consumer within the restaurant, wherein the authorizationtransaction includes a first bill amount, an employee identifier, amanager identifier, and a table identifier. The processor is furtherprogrammed to receive, as part of the POS data feed, a clearing messagematched to the authorization message wherein the clearing messageincludes a second bill amount, calculate a tip size based on theauthorization message and the clearing message, compare the tip size toa threshold tip size range, and when the tip size falls outside of thethreshold tip size range, transmit an alert to a device associated withthe manager identifier.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1-10 show example embodiments of the methods and systems describedherein.

FIG. 1 is a schematic diagram illustrating an example multi-partypayment card system for enabling payment-by-card transactions andgenerating aggregated merchant analytics in accordance with oneembodiment of the present disclosure.

FIG. 2 is an expanded block diagram of an example embodiment of acomputer system used in processing payment transactions that includes agratuity analytics computing device in accordance with one exampleembodiment of the present disclosure.

FIG. 3 illustrates an example configuration of a server system such asthe gratuity analytics computing device of FIG. 2.

FIG. 4 illustrates an example configuration of a client system shown inFIG. 2.

FIG. 5 is a simplified data flow diagram for generating gratuityanalytics using the gratuity analytics computing device of FIG. 2.

FIG. 6 is a data flow block diagram illustrating an example process ofcalculating and analyzing gratuity data in accordance with an exampleembodiment of the present disclosure.

FIG. 7 is a simplified diagram of an example method for generatingmerchant analytics and displaying the analytics on a user interfaceusing the merchant analytics computing device of FIG. 2.

FIG. 8 is a diagram of components of one or more example computingdevices that may be used in the environment shown in FIG. 2.

FIGS. 9 and 10 are example screenshots of a user interface of a usercomputing device, including gratuity analytics generated by the gratuityanalytics computing device of FIG. 2.

Although specific features of various embodiments may be shown in somedrawings and not in others, this is for convenience only. Any feature ofany drawing may be referenced and/or claimed in combination with anyfeature of any other drawing.

DETAILED DESCRIPTION

The systems and methods described herein are directed to generatinggratuity analytics for one or more restaurants. In one embodiment, thesystem is associated with or integral to a payment processing networkincluding a payment processor configured to process transactionsinitiated by cardholders using payment cards (e.g., credit cards, debitcard, prepaid cards, computing devices for storing payment accountinformation, etc.). The payment processor collects transaction dataassociated with these transactions for further processing. In theexample embodiment, the system includes a gratuity analytics computingdevice configured to process the transaction data to calculate tip datausing transaction data from the payment processor, and to generategratuity analytics based on the tip data.

As used herein, “restaurant” refers generally to any merchant servingfood and/or beverages, such as fast food restaurants, “sit-down”restaurants, bars, eating establishments within other merchant locations(e.g., a café within a bookstore), etc. Although the term restaurant isused within this application, there may be other uses for thisapplication where restaurant could be any industry where tips arereceived (e.g., spa, tour guide, maid service). In at least some cases,an average tip size for a particular restaurant may serve as a proxy forquality of service and/or for financial success (or lack thereof).

In the example embodiment described herein, the gratuity analyticscomputing device calculates a tip amount for each transaction initiatedat the restaurant using authorization data and clearing data. In orderto pay for a bill at a restaurant, a customer may provide their accountinformation, for example, a payment card account associated with apayment card such as a credit card or a debit card. An employee, in thisexample, the waiter or waitress, may input the customer's accountinformation by swiping (or otherwise communicating) a payment cardthrough a point-of-sale (POS) device. The input account information maybe transmitted to, for example, a payment processor, an issuing bank,and the like, to authorize payment of the bill for the meal. Thisinitial input includes authorization data of a purchase of a meal, andtypically includes the base price of the meal. Upon successfulauthorization of the base price by the issuing bank, a receipt may beprinted out or some other form of confirmation may be generated. In thisexample, the customer may add a gratuity to the base price by insertingthe gratuity into the receipt or confirmation. That is, after theinitial authorization for the base price of the meal, the customer hasthe opportunity to add a gratuity. When the customer adds a gratuity andadds their signature to or otherwise authorizes the final bill, theemployee will typically enter the total payment amount or the totalamount may be automatically entered. Here, the total amount includes thetip plus the base price. In this example, the total amount may also besent to the payment processor as a final amount. For example, a clearingmessage or clearing data including the final total may be transmitted tothe payment processor.

In some embodiments, the gratuity analytics computing device is inreal-time communication with the POS device. For example, the gratuityanalytics computing device receives a real-time data feed from the POSdevice. POS integration allows real time triggers and alerts to therestaurant management about customer happiness (while the customer isstill on the premises) and/or server performance. For example, thegratuity analytics computing device calculates tip percentages based onthe real-time POS data feed and sends an alert to a manager device ifthe tip percentage for a transaction or a group transaction (e.g.,entered by a specific employee) is below a predetermined threshold.

In other embodiments, the gratuity analytics computing device is not inreal-time communication with a POS device. The gratuity computing devicemay be in communication with the payment processor and receivehistorical transaction data. A non-POS option will still allow theapplication to compare manager performance by combining manager datafrom restaurant systems with transaction data. For example, the non-POSoption may use the historical transaction data to calculate tippercentages for a plurality of servers. This calculation may usehistorical data within a specified time range. For example, tip amountsmay be higher at 8 PM than at LOAM. The servers' tip data may be used todetermine the performance of the manager.

The gratuity analytics computing device may receive both authorizationdata and clearing data from the payment processor. The gratuityanalytics computing device may determine that the authorization data forthe base price of the meal and the clearing data for the total amount ofthe meal (i.e., base price plus tip) correspond to the same transaction.For example, the gratuity analytics computing device may perform aclearing/authorization matching process that links togetherauthorization messages with respective clearing messages based on one ormore transaction identifiers included in the authorizations (e.g.,messages) and the clearings (e.g., messages). Accordingly, the gratuityanalytics computing device may determine that authorization data matchesor corresponds to clearing data, and further compare an authorizationtransaction amount included in the authorization data with a clearingtransaction amount included in the clearing data to determine a tipamount.

In a first embodiment, the gratuity analytics computing device performsthis calculation of tip amounts on historical transaction data. Thegratuity analytics computing device receives a date range from a clientcomputing device. The client computing device may be any computingdevice capable of communicating over a communication link (e.g., theInternet) with the gratuity analytics computing device, including, forexample, a personal computer, a server computer, a smartphone, or atablet. The date range is input to the client computing device by a user(e.g., a user associated with a particular restaurant of a chain ofrestaurants) to view gratuity analytics associated with that date range.The date range may be as small as a month and may be as large as anumber of years. In some embodiments, the scale of the date rangeaffects the scale of the determined gratuity analytics. For example, ifa date range of a number of years is received, the gratuity analyticscomputing device may generate gratuity analytics on a month-by-monthscale. If a date range of a number of months (e.g., less than a year) isreceived, the gratuity analytics computing device may generate gratuityanalytics on a month-by-month and/or a week-by-week scale.

The gratuity analytics computing device receives or retrievestransaction data for the received date range. The transaction data isreceived or retrieved from, for example, a payment processor and/orassociated database configured to store transaction data. Thetransaction data is associated with a plurality of financialtransactions initiated within the received date range. The transactionsmay be further associated (e.g., stored and linked within database) withone particular restaurant and/or with a particular chain of restaurants,which may be identified by the user of the client computing device. Thegratuity analytics computing device may further retrieve transactiondata that is associated with one or more competitor restaurants, asdescribed further herein. The transaction data includes data elementssuch as a manager identifier, time stamp, and employee identifierassociated with each of the financial transactions. The transaction dataalso includes the authorization transactions and clearing transactions,described above. The gratuity analytics computing device performs theabove-described matching process to determine a tip amount for eachmatched transaction, wherein each matched transaction represents onemeal or bill paid for at the restaurant.

The merchant analytics computing device may calculate an average,median, and/or other aggregated or collective tip size for the merchant,based on the plurality of individual calculated tip amounts, asdescribed above. “Tip size” may refer to an actual numerical amount(e.g., $5.75) and/or to a percentage that the numerical tip amountrepresents of the total bill (e.g., 16%).

In various embodiments, the gratuity analytics computing device isconfigured to determine relative tip sizes according to a plurality ofmetrics, including time intervals (e.g., days of the week or particulartime intervals within a day), manager identifier, and/or employeeidentifier. In other words, the gratuity analytics computing device isconfigured to divide the received transaction data into a plurality ofsubsets according to at least one of the above-identified metrics andgenerate gratuity analytics such as tip size for each of the subsets forcomparison. The gratuity analytics computing device may generategratuity analytics for a plurality of time intervals such that thosetime intervals may be compared to one another, for example, to determinea most and/or least successful time interval, or to determine a trendover a plurality of adjacent or subsequent time intervals, etc. Thegratuity analytics computing device may use the manager identifier foreach matched transaction to divide the transaction data according tomanager. The gratuity analytics computing device may then generategratuity analytics for each individual manager at a particularrestaurant for comparison to one another, either within a particulartime interval (e.g., which manager was more successful within aparticular day or weekend) and/or overall. Similarly, the gratuityanalytics computing device may use the employee identifier for eachmatched transaction to divide the transaction data according to employee(e.g., server). The gratuity analytics computing device may thengenerate gratuity analytics for each individual employee at a particularrestaurant for comparison to one another, either within a particulartime interval (e.g., which employee was more successful within aparticular day or weekend) and/or overall.

The gratuity analytics computing device may further generate gratuityanalytics such as average tip size for a plurality of individualrestaurants within a chain, or “inter-location” gratuity analytics, forcomparison of restaurant locations within the chain. This comparison isuseful to, for example, a Chief Financial Officer or Chief MarketingOfficer, in order to determine the best and worst performing restaurantlocations within a chain.

The gratuity analytics computing device may further generate gratuityanalytics for one or more competitor restaurants and/or restaurantchains. The gratuity analytics computing device may identify one or morecompetitors and retrieve transaction data therefor to generate theaverage tip size analytics described above. The gratuity analyticscomputing device may identify the competitor(s) by, for example,referencing a list or table. The list or table may be generated (by thegratuity analytics computing device and/or by another, separatecomputing device) by determining an average ticket size (e.g., averagebill amount) and/or sales volume for the restaurant; determining theaverage ticket size and/or sales volume for a plurality of potentialcompetitor restaurants; and identifying one or more (actual) competitorrestaurants as those of the potential competitor restaurants with themost similar average ticket size and/or sales volume. The gratuityanalytics computing device may therefore generate gratuity analytics(e.g., comparative average tip size or comparative average ticket size)between a restaurant/chain and its competitor(s).

In some embodiments, the gratuity analytics computing device is furtherconfigured to generate card analytics associated with the restaurant. Inthese embodiments, the transaction data includes a card type of eachpayment card used to initiate a respective one of the financialtransactions represented by the transaction data. “Card type” mayinclude an issuer identifier (e.g., Citibank), a loyalty identifier(e.g., a particular points or rewards program), and/or a levelidentifier (e.g., basic, corporate, business, premium, gold/silver,etc.). The gratuity analytics computing device may identify at least onecommonly used card type. A commonly used card type may be defined as,for example, one or more card types with the highest usage frequencyand/or one or more card types that exceed a particular usage frequency(e.g., 100 uses or 1% of the total uses). The gratuity analyticscomputing device may then identify the associated issuer loyalty programetc. to the restaurant, such that the restaurant may approach thecorresponding issuer loyalty program etc. with one or more offers topresent to cardholders having a payment card of that corresponding cardtype.

In another embodiment, the gratuity analytics computing device performsreal-time calculation of tip amounts. In this embodiment, which is notexclusive of the above-described first embodiment, the gratuityanalytics computing device receives a real-time point-of-sale (POS) datafeed. This POS data feed comes from a merchant POS, for example, locatedwithin the actual restaurant. When an employee swipes a consumer's cardor otherwise authorizes a transaction for a bill to be paid for, anauthorization transaction (described above) is generated. Theauthorization transaction includes data elements such as a first billamount, an employee identifier, a manager identifier, and a tableidentifier. In many cases, the employee enters a tip amount soon after,generating an associated clearing transaction for the same bill. Becausethe gratuity analytics computing device is receiving the real-time POSfeed, the gratuity analytics computing device receives this clearingtransaction, including a second bill amount equal to the first billamount plus the tip amount, in real-time. Therefore, the gratuityanalytics computing device can calculate the tip amount for the bill inreal-time.

The gratuity analytics computing device compares the calculated tipamount to a threshold tip range (e.g., 10%-20%), the threshold tip rangerepresenting a typical or “acceptable” range of tip amounts. This rangemay be predefined, for example, by a manager of the restaurant.Alternatively, the gratuity analytics computing device may calculate andmaintain the threshold tip range, for example, by calculating onestandard deviation from an average tip size. When the calculated tipamount falls outside of the threshold tip range, the gratuity analyticscomputing device transmits an alert (e.g., a text, email, and/or app“push” notification) to a device associated with the manager identifier(e.g., the manager's phone). The alert may include the employeeidentifier associated with the bill and/or the table identifierassociated with the bill. In this embodiment, the manager is enabled totake immediate action for a particular low or high tip amount, while theassociated consumer is still likely in the restaurant. Thus, forexample, the manager may be able to talk to the consumer about theirparticularly poor (or particularly great) experience.

The gratuity analytics computing device is further configured tofacilitate the display of an interactive graphical user interface (UI).The UI may be displayed on a client computing device of a user. The UIis configured such that the user may easily view the generated gratuityanalytics, for example, on a dashboard accessible through an app and/ora web browser. The UI enables the user to select the date range and/orrestaurant(s) of interest, such that the gratuity analytics computingdevice may perform the processes described above to generate relevantanalytics.

The following detailed description of the embodiments of the disclosurerefers to the accompanying drawings. The same reference numbers indifferent drawings may identify the same or similar elements. Also, thefollowing detailed description does not limit the claims.

Described herein are computer systems such as gratuity analyticscomputing devices. As described herein, all such computer systemsinclude a processor and a memory. However, any processor in a computerdevice referred to herein may also refer to one or more processorswherein the processor may be in one computing device or a plurality ofcomputing devices acting in parallel. Additionally, any memory in acomputer device referred to herein may also refer to one or morememories wherein the memories may be in one computing device or aplurality of computing devices acting in parallel.

As used herein, a processor may include any programmable systemincluding systems using micro-controllers, reduced instruction setcircuits (RISC), application specific integrated circuits (ASICs), logiccircuits, and any other circuit or processor capable of executing thefunctions described herein. The above examples are example only, and arethus not intended to limit in any way the definition and/or meaning ofthe term “processor.”

As used herein, the term “database” may refer to either a body of data,a relational database management system (RDBMS), or to both. As usedherein, a database may include any collection of data includinghierarchical databases, relational databases, flat file databases,object-relational databases, object oriented databases, and any otherstructured collection of records or data that is stored in a computersystem. The above examples are example only, and thus are not intendedto limit in any way the definition and/or meaning of the term database.Examples of RDBMS's include, but are not limited to including, Oracle®Database, MySQL, IBM® DB2, Microsoft® SQL Server, Sybase®, andPostgreSQL. However, any database may be used that enables the systemsand methods described herein. (Oracle is a registered trademark ofOracle Corporation, Redwood Shores, Calif.; IBM is a registeredtrademark of International Business Machines Corporation, Armonk, N.Y.;Microsoft is a registered trademark of Microsoft Corporation, Redmond,Wash.; and Sybase is a registered trademark of Sybase, Dublin, Calif.)

In one embodiment, a computer program is provided, and the program isembodied on a computer readable medium. In an example embodiment, thesystem is executed on a single computer system, without requiring aconnection to a sever computer. In a further embodiment, the system isbeing run in a Windows® environment (Windows is a registered trademarkof Microsoft Corporation, Redmond, Washington). In yet anotherembodiment, the system is run on a mainframe environment and a UNIX®server environment (UNIX is a registered trademark of X/Open CompanyLimited located in Reading, Berkshire, United Kingdom). The applicationis flexible and designed to run in various different environmentswithout compromising any major functionality. In some embodiments, thesystem includes multiple components distributed among a plurality ofcomputing devices. One or more components may be in the form ofcomputer-executable instructions embodied in a computer-readable medium.

As used herein, an element or step recited in the singular and proceededwith the word “a” or “an” should be understood as not excluding pluralelements or steps, unless such exclusion is explicitly recited.Furthermore, references to “example embodiment” or “one embodiment” ofthe present disclosure are not intended to be interpreted as excludingthe existence of additional embodiments that also incorporate therecited features.

As used herein, the terms “software” and “firmware” are interchangeable,and include any computer program stored in memory for execution by aprocessor, including RAM memory, ROM memory, EPROM memory, EEPROMmemory, and non-volatile RAM (NVRAM) memory. The above memory types areexample only, and are thus not limiting as to the types of memory usablefor storage of a computer program.

As used herein, the terms “payment device,” “transaction card,”“financial transaction card,” and “payment card” refer to any suitabletransaction card, such as a credit card, a debit card, a prepaid card, acharge card, a membership card, a promotional card, a frequent flyercard, an identification card, a prepaid card, a gift card, and/or anyother device that may hold payment account information, such as mobilephones, Smartphones, personal digital assistants (PDAs), wearablecomputing devices, key fobs, and/or any other computing devices capableof providing account information. Moreover, these terms may refer topayments made directly from or using bank accounts, stored valuedaccounts, mobile wallets, etc., and accordingly are not limited tophysical devices but rather refer generally to payment credentials. Eachtype of payment device can be used as a method of payment for performinga transaction. In addition, consumer card account behavior can includebut is not limited to purchases, management activities (e.g., balancechecking), bill payments, achievement of targets (meeting accountbalance goals, paying bills on time), and/or product registrations(e.g., mobile application downloads).

The systems and processes are not limited to the specific embodimentsdescribed herein. In addition, components of each system and eachprocess can be practiced independent and separate from other componentsand processes described herein. Each component and process also can beused in combination with other assembly packages and processes.

The following detailed description illustrates embodiments of thedisclosure by way of example and not by way of limitation. It iscontemplated that the disclosure has general application to thegeneration and communication (e.g., display) of aggregate gratuityanalytics.

FIG. 1 is a schematic diagram illustrating an example multi-partypayment card system 20 for enabling payment-by-card transactions andcommunicating aggregated gratuity analytics for a sector, in accordancewith one embodiment of the present disclosure. FIG. 1 depicts a flow ofdata in a typical financial transaction through system 20, whichincludes a gratuity analytics computing device 112. Components of system20 provide gratuity analytics computing device 112 with transactiondata, which gratuity analytics computing device 112 processes togenerate gratuity analytics and provide the analytics on a userinterface.

Embodiments described herein may relate to a transaction card system,such as a credit card payment system using the MasterCard® interchangenetwork. The MasterCard® interchange network is a set of proprietarycommunications standards promulgated by MasterCard InternationalIncorporated® for the exchange of financial transaction data and thesettlement of funds between financial institutions that are members ofMasterCard International Incorporated®. (MasterCard is a registeredtrademark of MasterCard International Incorporated located in Purchase,N.Y.).

In a typical transaction card system, a financial institution called the“issuer” issues a transaction card, such as a credit card, to a consumeror cardholder 22, who uses the transaction card to tender payment for apurchase from a merchant 24. Cardholder 22 may purchase goods andservices (“products”) at merchant 24. Cardholder 22 may make suchpurchases using virtual forms of the transaction card and, morespecifically, by providing data related to the transaction card (e.g.,the transaction card number, expiration date, associated postal code,and security code) to initiate transactions. To accept payment with thetransaction card or virtual forms of the transaction card, merchant 24must normally establish an account with a financial institution that ispart of the financial payment system. This financial institution isusually called the “merchant bank,” the “acquiring bank,” or the“acquirer.” When cardholder 22 tenders payment for a purchase with atransaction card or virtual transaction card, merchant 24 requestsauthorization from a merchant bank 26 for the amount of the purchase.The request may be performed over the telephone or electronically, butis usually performed through the use of a point-of-sale terminal, whichreads cardholder's 22 account information from a magnetic stripe, achip, or embossed characters on the transaction card and communicateselectronically with the transaction processing computers of merchantbank 26. Merchant 24 receives cardholder's 22 account information asprovided by cardholder 22. Alternatively, merchant bank 26 may authorizea third party to perform transaction processing on its behalf. In thiscase, the point-of-sale terminal will be configured to communicate withthe third party. Such a third party is usually called a “merchantprocessor,” an “acquiring processor,” or a “third party processor.”

Using an interchange network 28, computers of merchant bank 26 ormerchant processor will communicate with computers of an issuer bank 30to determine whether cardholder's 22 account 32 is in good standing andwhether the purchase is covered by cardholder's 22 available creditline. Based on these determinations, the request for authorization willbe declined or accepted. If the request is accepted, an authorizationcode is issued to merchant 24.

When a request for authorization is accepted, the available credit lineof cardholder's 22 account 32 is decreased. Normally, a charge for apayment card transaction is not posted immediately to cardholder's 22account 32 because bankcard associations, such as MasterCardInternational Incorporated®, have promulgated rules that do not allowmerchant 24 to charge, or “capture,” a transaction until products areshipped or services are delivered. However, with respect to at leastsome debit card transactions, a charge may be posted at the time of thetransaction. When merchant 24 ships or delivers the products orservices, merchant 24 captures the transaction by, for example,appropriate data entry procedures on the point-of-sale terminal. Thismay include bundling of approved transactions daily for standard retailpurchases. If cardholder 22 cancels a transaction before it is captured,a “void” is generated. If cardholder 22 returns products after thetransaction has been captured, a “credit” is generated. Interchangenetwork 28 and/or issuer bank 30 stores the transaction cardinformation, such as a type of merchant, amount of purchase, date ofpurchase, in a database 120 (shown in FIG. 2).

After a purchase has been made, a clearing process occurs to transferadditional transaction data related to the purchase among the parties tothe transaction, such as merchant bank 26, interchange network 28, andissuer bank 30. More specifically, during and/or after the clearingprocess, additional data, such as a time of purchase, a merchant name, atype of merchant, purchase information, cardholder account information,a type of transaction, information regarding the purchased item and/orservice, and/or other suitable information, is associated with atransaction and transmitted between parties to the transaction astransaction data, and may be stored by any of the parties to thetransaction. In the example embodiment, transaction data including suchadditional transaction data may also be provided to systems includinggratuity analytics computing device 112. In the example embodiment,interchange network 28 provides such transaction data (includingmerchant data associated with merchant tenants of each commercial realestate asset of each portfolio record) and additional transaction data.In alternative embodiments, any party may provide such data to gratuityanalytics computing device 112.

After a transaction is authorized and cleared, the transaction issettled among merchant 24, merchant bank 26, and issuer bank 30.Settlement refers to the transfer of financial data or funds amongmerchant's 24 account, merchant bank 26, and issuer bank 30 related tothe transaction. Usually, transactions are captured and accumulated intoa “batch,” which is settled as a group. More specifically, a transactionis typically settled between issuer bank 30 and interchange network 28,and then between interchange network 28 and merchant bank 26, and thenbetween merchant bank 26 and merchant 24.

As described below in more detail, gratuity analytics computing device112 may be used to generate and communicate aggregated gratuityanalytics. Although the systems described herein are not intended to belimited to facilitate such applications, the systems are described assuch for exemplary purposes.

FIG. 2 is an expanded block diagram of an example embodiment of acomputer system 100 used in processing payment transactions thatincludes gratuity analytics computing device 112 in accordance with oneexample embodiment of the present disclosure. In the example embodiment,system 100 is used for generating gratuity analytics and displaying theanalytics on a user interface, as described herein.

More specifically, in the example embodiment, system 100 includes agratuity analytics computing device 112, and a plurality of clientsub-systems, also referred to as client systems 114, connected togratuity analytics computing device 112. In one embodiment, clientsystems 114 are computers including a web browser, such that gratuityanalytics computing device 112 is accessible to client systems 114 usingthe Internet and/or using network 115. Client systems 114 areinterconnected to the Internet through many interfaces including anetwork 115, such as a local area network (LAN) or a wide area network(WAN), dial-in-connections, cable modems, special high-speed IntegratedServices Digital Network (ISDN) lines, and RDT networks. Client systems114 may include systems associated with cardholders 22 (shown in FIG. 1)as well as external systems used to store data. Gratuity analyticscomputing device 112 is also in communication with payment network 28using network 115. Further, client systems 114 may additionallycommunicate with payment network 28 using network 115. Client systems114 could be any device capable of interconnecting to the Internetincluding a web-based phone, PDA, or other web-based connectableequipment.

A database server 116 is connected to database 120, which containsinformation on a variety of matters, as described below in greaterdetail. In one embodiment, centralized database 120 is stored ongratuity analytics computing device 112 and can be accessed by potentialusers at one of client systems 114 by logging onto gratuity analyticscomputing device 112 through one of client systems 114. In analternative embodiment, database 120 is stored remotely from gratuityanalytics computing device 112 and may be non-centralized. Database 120may be a database configured to store information used by gratuityanalytics computing device 112 including, for example, transaction data,defined sectors, merchant definitions, user data, portfolio records,merchant scores, and sector scores.

Database 120 may include a single database having separated sections orpartitions, or may include multiple databases, each being separate fromeach other. Database 120 may store transaction data generated over theprocessing network including data relating to merchants, consumers,account holders, prospective customers, issuers, acquirers, and/orpurchases made. Database 120 may also store account data including atleast one of a cardholder name, a cardholder address, an account number,other account identifiers, and transaction information. Database 120 mayalso store merchant information including a merchant identifier thatidentifies each merchant registered to use the network, and instructionsfor settling transactions including merchant bank account information.Database 120 may also store purchase data associated with items beingpurchased by a cardholder from a merchant, and authorization requestdata.

In the example embodiment, one of client systems 114 may be associatedwith one of acquirer bank 26 (shown in FIG. 1) and issuer bank 30 (alsoshown in FIG. 1). For example, one of client systems 114 may be a POSdevice. Client systems 114 may additionally or alternatively beassociated with a user (e.g., a commercial real estate owner or lender,a marketing director, a consumer, or any other end user). In the exampleembodiment, one of client systems 114 includes a user interface 118. Forexample, user interface 118 may include a graphical user interface withinteractive functionality, such that aggregated gratuity analytics,transmitted from gratuity analytics computing device 112 to clientsystem 114, may be shown in a graphical format. A user of client system114 may interact with user interface 118 to view, explore, and otherwiseinteract with the gratuity analytics. Gratuity analytics computingdevice 112 may be associated with interchange network 28 and/or mayprocess transaction data.

FIG. 3 illustrates an example configuration of a server system 301 suchas gratuity analytics computing device 112 (shown in FIGS. 1 and 2) usedto generate gratuity analytics and present said analytics on aninteractive user interface, in accordance with one example embodiment ofthe present disclosure. Server system 301 may also include, but is notlimited to, database server 116. In the example embodiment, serversystem 301 determines and analyzes characteristics of devices used inpayment transactions, as described below.

Server system 301 includes a processor 305 for executing instructions.Instructions may be stored in a memory area 310, for example. Processor305 may include one or more processing units (e.g., in a multi-coreconfiguration) for executing instructions. The instructions may beexecuted within a variety of different operating systems on the serversystem 301, such as UNIX, LINUX, Microsoft Windows®, etc. It should alsobe appreciated that upon initiation of a computer-based method, variousinstructions may be executed during initialization. Some operations maybe required in order to perform one or more processes described herein,while other operations may be more general and/or specific to aparticular programming language (e.g., C, C#, C++, Java, or othersuitable programming languages, etc.).

Processor 305 is operatively coupled to a communication interface 315such that server system 301 is capable of communicating with a remotedevice such as a user system or another server system 301. For example,communication interface 315 may receive requests (e.g., requests todisplay gratuity analytics and/or provide an interactive user interface)from a client system 114 via the Internet, as illustrated in FIG. 2.

Processor 305 may also be operatively coupled to a storage device 134.Storage device 134 is any computer-operated hardware suitable forstoring and/or retrieving data. In some embodiments, storage device 134is integrated in server system 301. For example, server system 301 mayinclude one or more hard disk drives as storage device 134. In otherembodiments, storage device 134 is external to server system 301 and maybe accessed by a plurality of server systems 301. For example, storagedevice 134 may include multiple storage units such as hard disks orsolid state disks in a redundant array of inexpensive disks (RAID)configuration. Storage device 134 may include a storage area network(SAN) and/or a network attached storage (NAS) system.

In some embodiments, processor 305 is operatively coupled to storagedevice 134 via a storage interface 320. Storage interface 320 is anycomponent capable of providing processor 305 with access to storagedevice 134. Storage interface 320 may include, for example, an AdvancedTechnology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, aSmall Computer System Interface (SCSI) adapter, a RAID controller, a SANadapter, a network adapter, and/or any component providing processor 305with access to storage device 134.

Memory area 310 may include, but are not limited to, random accessmemory (RAM) such as dynamic RAM (DRAM) or static RAM (SRAM), read-onlymemory (ROM), erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), andnon-volatile RAM (NVRAM). The above memory types are exemplary only, andare thus not limiting as to the types of memory usable for storage of acomputer program.

FIG. 4 illustrates an example configuration of a client computing device402. Client computing device 402 may include, but is not limited to,client systems (“client computing devices”) 114. Client computing device402 includes a processor 404 for executing instructions. In someembodiments, executable instructions are stored in a memory area 406.Processor 404 may include one or more processing units (e.g., in amulti-core configuration). Memory area 406 is any device allowinginformation such as executable instructions and/or other data to bestored and retrieved. Memory area 406 may include one or morecomputer-readable media.

Client computing device 402 also includes at least one media outputcomponent 408 for presenting information to a user 400. Media outputcomponent 408 is any component capable of conveying information to user400. In some embodiments, media output component 408 includes an outputadapter such as a video adapter and/or an audio adapter. An outputadapter is operatively coupled to processor 404 and operativelycouplable to an output device such as a display device (e.g., a liquidcrystal display (LCD), organic light emitting diode (OLED) display,cathode ray tube (CRT), or “electronic ink” display) or an audio outputdevice (e.g., a speaker or headphones).

In some embodiments, client computing device 402 includes an inputdevice 410 for receiving input from user 400. Input device 410 mayinclude, for example, a keyboard, a pointing device, a mouse, a stylus,a touch sensitive panel (e.g., a touch pad or a touch screen), a camera,a gyroscope, an accelerometer, a position detector, and/or an audioinput device. A single component such as a touch screen may function asboth an output device of media output component 408 and input device410.

Client computing device 402 may also include a communication interface412, which is communicatively couplable to a remote device such asserver system 302 or a web server operated by a merchant. Communicationinterface 412 may include, for example, a wired or wireless networkadapter or a wireless data transceiver for use with a mobile phonenetwork (e.g., Global System for Mobile communications (GSM), 3G, 4G orBluetooth) or other mobile data network (e.g., WorldwideInteroperability for Microwave Access (WIMAX)).

Stored in memory area 406 are, for example, computer-readableinstructions for providing a user interface to user 400 via media outputcomponent 408 and, optionally, receiving and processing input from inputdevice 410. A user interface may include, among other possibilities, aweb browser and client application. Web browsers enable users 400 todisplay and interact with media and other information typically embeddedon a web page or a website from a web server associated with a merchant.A client application allows users 400 to interact with a serverapplication associated with, for example, a merchant. The userinterface, via one or both of a web browser and a client application,facilitates display of generated gratuity analytics by gratuityanalytics computing device 112. The user may interact with the userinterface to view and explore the gratuity analytics, for example, byselecting a sector of interest using input device 410 and viewinganalytics associated with that sector.

FIG. 5 is a simplified data flow diagram for generating aggregatedgratuity analytics for merchants, and providing the analytics fordisplay on a user interface using gratuity analytics computing device112. Gratuity analytics computing device 112 includes a receiver 580, acalculator 582, an analyzer 584, a transmitter 586, and a processor 588.Although not illustrated, gratuity analyzing computing device 112 mayinclude other components, for example, a memory, a display, an inputunit, and the like. As described herein, gratuity analytics computingdevice 112 is in communication with POS device 510. Gratuity analyticscomputing device 112 receives a real-time POS data feed 520 from POSdevice 510. In other embodiments, gratuity analytics computing device112 may be in communication with payment processor 28. Gratuityanalytics computing device 112 receives historical transaction data 540from payment processor 28. In some embodiments, gratuity analyticscomputing device 112 may be in communication with POS device 510 andpayment processor 28. Gratuity analytics computing device 112 maycommunicate with the payment processor 28 and/or the POS device 510through an application.

Receiver 580 may receive historical transaction data 540 and/orreal-time POS data feed 520 for use in calculating and analyzinggratuity information of a plurality of cardholders who made purchases ata plurality of merchants in a plurality of locations. Merchants mayinclude restaurants associated with the user and competitors ofrestaurants associated with the user. For example, receiver 580 mayreceive authorization data and clearing data of a plurality oftransactions. The authorization data and the clearing data may be storedlocally or may be stored remotely, for example, in a data warehouse ofpayment processor 28. In some examples, receiver 580 may receive thehistorical transaction data 540 and/or real-time POS data feed 520 inresponse to a query transmitted by transmitter 586 to a remote devicesuch as the data warehouse. The historical transaction data 540 and/orreal-time POS data feed 520 received by receiver 580 may includetransactions that occur during a predetermined period of time, forexample, over a week of time, a month of time, a year of time, and thelike. The historical transaction data 540 and/or real-time POS data feed520 may include a first bill amount (e.g., authorization amount), asecond bill amount (e.g., clearing amount), an employee ID, a managerID, a table ID, a merchant ID, and a time stamp.

The receiver 580 may also receive input from various users via a clientcomputing device 114. For example, the receiver 580 may receive an inputfrom a user via the client computing device 114 such as a desktopcomputer, a mobile device, a personal digital assistant (PDA), a tablet,an MP3 player, a laptop computer, and the like. The input from the usermay be received via a wired connection, a wireless connection, or acombination thereof. In response to receiving a user input such as daterange and location 530, the receiver 580 may store the user input in amemory (not shown) of the gratuity analyzing computing device 112.Furthermore, the user input may be used to control the analyzer 584 asis further explained herein.

Based on the received transaction data, the calculator 582 may matchauthorization transactions to clearing transactions, for example, basedon one or more transaction identifiers included within both theauthorization transactions and the clearing transactions. Calculator 582may determine a gratuity amount for a matched transaction based on adifference in a payment amount between a clearing transaction and anauthorization transaction. For example, the calculator 582 may determinea gratuity amount for all matched transactions, and store the gratuitydata locally or remotely along with the historical transaction data 540and real-time POS data feed 520.

Gratuity analytics 550 may be provided to a user via the clientcomputing device 114, for example, based on an input of the userreceived by the receiver 580. For example, the receiver 580 may receivea request from the client computing device 114 for merchants within aspecific location. In response, analyzer 584 may analyze the gratuityamounts generated by calculator 582 and generate tip data aboutmerchants corresponding to the transactions who are within the specificlocation requested by the user. For example, analyzer 584 may determinean average tip amount for a particular merchant, a group of merchants, alocation, and the like. Analyzer 584 may also determine whether a tipamount, tip percentage, and the like are currently trending upward ordownward by comparing current tip data with previous tip data. As anon-limiting example, tip data of a merchant during a current month maybe compared with tip data of the merchant during the same month, in aprevious year, to determine whether the tip data is currently trendingupward or downward.

Transmitter 586 may transmit the analyzed tip data results to clientcomputing device 114, for example, through a wired and/or wirelessconnection. For example, a user may logon to a web browser controlled bya payment processor 28, an issuing bank, and the like, and receive tipdata by requesting tip data through the web browser. As another example,the user may have a mobile application stored on a mobile device of theuser. The payment processor 28, issuing bank, and the like, may have anapplication stored on a computing device thereof that supports themobile application stored on the mobile device of the user. Accordingly,the user may request tip data through the mobile application stored onthe user's mobile device.

Processor 588 may control the overall operation of gratuity analyzingcomputing device 112. Also, in some examples, the operation of one ormore of receiver 580, calculator 582, analyzer 584, and transmitter 586may be performed or controlled by processor 588.

Gratuity analytics computing device 112 may use real-time POS data feed520 to determine, at analyzer 584, when a tip amount or a tip percentassociated with an employee ID is outside of a threshold tip size range(e.g., if a tip for a server is outside the range of 10%-20%). In someembodiments, the threshold tip size range is determined by analyzing thehistorical transaction data 540. Historical transaction data 540includes past tip sizes and ticket sizes. Past tip sizes may be used toset the threshold tip range by calculating one standard deviation froman average tip size. In some embodiments, the threshold tip size rangemay be determined by the manager.

Gratuity analytics computing device 112 may transmit from thetransmitter 586 an alert 560 to a manager device 570 associated with amanager ID. The alert 560 is transmitted as one or more of a shortmessage service message, an e-mail, an application notification, or anyother suitable form of communication. The transmitted alert 560indicates that the bill of an extremely dissatisfied customer or anextremely satisfied customer has been processed. This indication isbased on the tip falling outside of the threshold tip size range (e.g.,10%-20% of the bill). The alert 560 communicated to the manager device570 may include the employee ID and the table ID associated with thecustomer. Alert 560 may allow the manager to respond to the customerwhile the customer is still in the restaurant. This allows the managerto discover the source of dissatisfaction or satisfaction.

FIG. 6 is a data flow block diagram illustrating an example of a process600 of calculating and analyzing gratuity analytics in accordance withan example embodiment of the present disclosure. Process 600 may analyzetransaction data over a period of time, and determine gratuity data 640based on the transaction data over the period of time. For example, thetransaction may be a purchase made using a payment card, for example, acredit card, a debit card, a prepaid card, a charge card, a membershipcard, a promotional card, a frequent flyer card, an identification card,a gift card, and/or any other device that may hold payment accountinformation, such as a mobile phone, a fob, and the like. Also, theperiod of time may be any desired amount of time, for example, a week, amonth, a year, two years, and the like. In addition, the period of timeis not necessarily the most recent period of time but may be, forexample, a period of time from a previous month, a previous year, aprevious two years, and the like.

In this example, authorization data 610 is generated from theauthorization process of approving or declining a transaction before apurchase is finalized or cash is disbursed. For example, theauthorization data 610 may be based on a purchase of items such as goodsand/or services from a merchant where a tip is customary to showsatisfaction with the service provided. As a non-limiting example, theitems may include a round of golf, a meal at a restaurant, a cab ride, aservice provided by a moving company, a barber shop, a hair salon, acleaning service, and the like. In this example, the authorization data610 includes one or more transaction identifiers about the transactionwhich may include an account number or a card number, a time and date ofthe transaction, a location, a merchant identification, a merchantcategory code, and the like. The authorization data 610 may be generatedin response to an employee of the merchant entering payment informationsuch as the payment card or payment account information of a purchaser.The authorization data 610 may be stored in a database, for example, adata warehousing system of a payment processor.

Clearing data 620 is generated from the clearing process of exchangingtransaction data between a payment processor and an issuing bank (i.e.,issuer). From the information provided in clearing, a payment processormay calculate the amounts for settlement. Clearing includes sendingtransactions from the processor to the issuer for posting to thecardholder's account (also known as “presentment”). The paymentprocessor gathers the information, edits it, assesses the appropriatefees, and sends it on to the appropriate receiver. The clearing messagescontain data but do not actually exchange or transfer funds. Like theauthorization data 610, the clearing data 620 also includes one or moretransaction identifiers. Also, the clearing data 620 may be stored in adatabase such as the data warehousing system of the payment processor.

Based on the authorization data. 610 and the clearing data 620, gratuityinformation may be calculated using a tip calculator 630, For example, apayment processor may query a database such as an authorization storageand a clearing storage of the data warehousing system to retrieveauthorization data 610 and clearing data 620 relating to transactionscarried out at one or more merchants at one or more locations in a giventime period, for example during the past month, during a past year,during a previous month, during a previous year, and the like. Thepayment processor may match the transaction identifiers of theauthorization data 610 to the transaction identifiers of the clearingdata 620 to generate a table of matched identifiers. Based on the tableof matched authorizations and clearings, the payment processor maydetermine gratuities paid by cardholders for the respectivetransactions, for example, based on a difference in payment amountbetween the clearing data 620 and the authorization data 610 of acorresponding transaction. The payment processor may determinegratuities for a number of transactions based on purchases by a numberof different users. The gratuity information may be stored by thepayment processor.

The payment processor may determine average gratuity analytics 640 for aspecific merchant or for a group of merchants of the same type within asimilar location. For example, the payment processor may calculate thegratuities left by cardholders at a particular merchant over aparticular amount of time, for example, a burger restaurant over thepast month. In this example, the payment processor may determine anaverage gratuity left by cardholders at the burger restaurant over thelast month and generate analytics 640. For example, the gratuityanalytics 640 may include a tip percentage, a tip amount, a currenttipping trend for the business, a current tipping trend for thegeographical location, a current tipping trend for the merchant ID, acurrent tipping trend based on the card type, and the like. The currenttipping trend may be calculated by comparing historical tipping datawith current tipping data and determining a trend based on thecomparison.

As another example, the payment processor may group together similartypes of merchants located in the same area, and determine averagegratuity information for the group of merchants of the same type withinthe same area. For example, the payment processor may group together aplurality of burger restaurants included in a particular zip code, andcalculate average gratuity data of the plurality of burger restaurantsbased on calculated gratuities at the burger restaurants in theparticular zip code instead of calculated gratuities at an individualburger restaurant. In this example, the gratuity information from aplurality of merchants is aggregated together and may provide an averagegratuity of that type of merchant in a particular location.

At 650, merchants or groups of merchants are compared with each otherand a ranking is provided at 670. In this example, the ranking isdetermined 650 based on tips and is provided 670 to user. Rankings 650may be based on and/or in response to an input of a user 660. Here, at660 the user may determine whether to be provided with tip data ofindividual merchants or groups of merchants. For example, the user mayinput one or more locations, a merchant type, and or other information.

For example, at 650, individual merchants located in a similar locationmay be compared with each other and a ranking of the merchants based ontip data may be provided (FIG. 5) at 670. This information may behelpful to someone looking for employment in a particular area. Asanother example, tip data of similar merchant types at the same locationmay be grouped together, and tip data of groups of similar types ofmerchants from different locations may be compared with each other at650 and provided to the user at 670. This information may be helpful tosomeone interested in opening a business of a same or similar type ofbusiness provided by the grouped merchants. As another example, tip dataof similar merchant types may be helpful to the CFO, COO, and CMO todetermine performance.

As described above and herein, in some embodiments, the system and/orcomponents thereof (e.g., a payment processor or gratuity analyzingcomputing device, FIG. 5) may store merchant identifiers and/orassociated payment amounts, gratuity amounts, base price amounts, andtotal amounts, and/or cardholder/account identifiers, without includingsensitive personal information, also known as personally identifiableinformation or PII, in order to ensure the privacy of individuals and/ormerchants associated with the stored data. Personally identifiableinformation may include any information capable of identifying anindividual. For privacy and security reasons, personally identifiableinformation may be withheld and only secondary identifiers may be used.For example, data received by the system and/or components thereof mayidentify user “John Smith” as user “ZYX123” without any method ofdetermining the actual name of user “ZYX123”. In some examples whereprivacy and security can otherwise be ensured (e.g., via encryption andstorage security), or where individuals consent, personally identifiableinformation may be received and used by the system. In situations inwhich the systems discussed herein collect personal information aboutindividuals and/or merchants, or may make use of such personalinformation, the individuals and/or merchants may be provided with anopportunity to control whether such information is collected or tocontrol whether and/or how such information is used. In addition,certain data may be processed in one or more ways before it is stored orused, so that personally identifiable information is removed. Forexample, an individual's identity may be processed so that no personallyidentifiable information can be determined for the individual, or anindividual's geographic location may be generalized where location datais obtained (such as to a city, ZIP code, or state level), so that aparticular location of an individual cannot be determined. Moreover,certain information about a particular merchant may be generalized oraggregated to information about an associated merchant type, to obscuremerchant-level data.

FIG. 7 is a diagram illustrating an example of a gratuity analyzingmethod 700 in accordance with an example embodiment of the presentdisclosure. Method 700 may be performed by a computing device associatedwith or corresponding to a payment processor. In this example,authorization data and clearing data of a plurality of transactions maybe received, in 710. For example, the authorization data and theclearing data may be received automatically based on a predeterminedtimer, at various intervals. As another example, the authorization dataand the clearing data may be received in response to a request or aquery for the authorization data and the clearing data that is receivedfrom the computing device. For example, the authorization data and theclearing data may be transaction data generated during a time periodthat may be identified by the query transmitted by the computing device.As a non-limiting example, the time period may be the previous week, theprevious month, the previous year, and the like. Also, to determinetrending information, the period of time information may include aperiod of time that is in the past, for example, a period of time from ayear ago, and the like.

Based on the authorization data and the clearing data, in 720authorization data from a respective transaction may be matched totransaction data from the same transaction. For example, a table may begenerated in which transaction identifiers included in authorizationdata from a plurality of transactions is respectively matched withtransaction identifiers included in clearing data from the plurality oftransactions. In this example, the computing device may determine thatan authorization message matches a clearing message based on one or moretransaction identifiers common to both the authorization message and theclearing message.

According to various examples, in 730, the method 700 may furtherinclude calculating tip data for the plurality of transactions. Based onthe matched transaction data, the payment processor may determinegratuities paid by cardholders for the respective transactions, forexample, based on a difference in payment amount between a total paymentamount of the clearing data and a total payment amount of theauthorization data of a corresponding transaction. The payment processormay determine gratuities for a plurality of transactions based onpurchases by a plurality of different users. Also, the gratuityinformation may be stored by the payment processor.

In 740 the calculated tip data may be analyzed and a plurality ofmerchants may be ranked against each other based on the calculated tipdata thereof. For example, in response to a user input for a particularlocation, tip data of a plurality of different merchants may be comparedwith each other, and a ranking from best to worst tip data may begenerated and provided to the user. In this example, the tip data mayinclude an average amount of tip, an average tip percentage, and/or anindication as to whether the tip percentage is trending up or down. Asanother example, tip data of a plurality of similar types of business ina particular location may be grouped together. For example, a type ofmerchant such as barbershops in the same neighborhood, zip code, city,county, and the like, may be analyzed and tip data from the barbershopsmay be aggregated to generate tip data for a type of business in aparticular location.

The aggregation may be performed for similar types of businesses.Accordingly, tip data of a group of merchants of a first type ofbusiness may be compared with tip data of a different group of merchantsof a second type of business located in the same location. As anotherexample, different locations may be compared with each other. Forexample, tip data from a group of merchants of a first type of businessin a first location may be aggregated. Also, tip data of a group ofmerchants of the first type of business but located in a second locationmay be aggregated. Accordingly, tip data of groups of businesses indifferent locations may be compared with each other. For example, tipdata of a group of hair salons located in Kansas City, Mo. may becompared with tip data of a group of hair salons located in St. Louis,Mo.

The compared and ranked tip data of the merchants from one or morelocations may be output to a user, in 750. For example, the ranked tipdata may be displayed through a web browser on a screen of a usercomputer. As another example, the ranked tip data may be transmitted toa mobile device of the user through a mobile application, and the rankedtip data may be displayed on a screen of the mobile device.

FIG. 8 is a diagram of components of one or more example computingdevices that may be used in the environment shown in FIG. 2. FIG. 8further shows a configuration of databases including at least database120 (shown in FIG. 2). Database 120 may store information such as, forexample, transaction data 802, POS Information 804, and data andlocation data 806. Database 120 is coupled to several separatecomponents within gratuity analytics computing device 112, which performspecific tasks.

Gratuity analytics computing device 112 includes a receiving component810, a gratuity calculation component 820, a gratuity analyticscomponent 830, and a display component 840. The receiving component 810for receives transaction data 802 for financial transactions. Thefinancial transactions may occur in real time or within a period oftime. The transaction data 802 is associated with a plurality ofmerchants. The gratuity calculation component 820 for using thetransaction data 802, the POS information 804, and/or the data andlocation data 806 for calculating tip data. The gratuity analyticscomponent 830 for analyzing the tip data. The displaying component 840(alternatively referred to as a “display component”) displays on a userinterface the aggregated gratuity analytics. The aggregated gratuityanalytics display is discussed further in FIGS. 9 and 10.

In some implementations, gratuity calculation component 820 (or anyother component of gratuity analytics computing device 112) may befurther configured to calculate a growth of a particular restaurant or agroup of restaurants using received transaction data 802. Therestaurants may be the same chain of restaurants, a particular type ofrestaurants, or restaurants within a defined location. The growthrepresents a difference in average tip from a beginning of the period oftime to an end of the period of time. Gratuity analytics component 830may be further configured to determine a relative ranking by comparingthe growth of each merchant to the plurality of merchants and generatethe growth score for each merchant based on the relative ranking.

In some implementations, gratuity calculation component 820 (or anyother component of gratuity analytics computing device 112) may befurther configured to calculate a stability of each merchant usingreceived transaction data 802 for a subset of the plurality of merchantslocated in a predetermined area. The stability represents maintenance ofaverage tip size during the period of time. Gratuity analytics component830 may be further configured to determine a relative ranking for eachmerchant by comparing the stability of each merchant of the plurality ofmerchants, and generate the stability score for each merchant based onthe relative ranking.

In some implementations, gratuity calculation component 820 (or anyother component of gratuity analytics computing device 112) may befurther configured to calculate a size of each merchant using receivedtransaction data 802 for a subset of the plurality of merchants. Thesize represents a total tip size in each merchant during the period oftime. Gratuity analytics component 830 may be further configured todetermine a relative ranking for each merchant by comparing the size ofeach merchants of the plurality of merchant, and generate the size scorefor each merchant based on the relative ranking.

In some implementations, gratuity calculation component 820 (or anyother component of gratuity analytics computing device 112) may befurther configured to calculate a traffic score of each merchant usingreceived transaction data 802 for a subset of the plurality ofmerchants. The traffic score represents a number of transactionsinitiated in each merchant during the period of time. Gratuity analyticscomponent 820 may be further configured to determine a relative rankingfor each merchant by comparing the traffic score of each merchant of theplurality of merchants, and generate the traffic score ranking for eachmerchant based on the relative ranking.

In some implementations, gratuity calculation component 820 (or anyother component of gratuity analytics computing device 112) may befurther configured to calculate an average ticket size for each merchantusing received transaction data 802 for a subset of the plurality ofmerchants. The average ticket size represents an average transactionamount in each merchant during the period of time, and the averageticket size may be calculated by dividing a total sales revenue for amerchant by a number of transactions initiated in the merchant duringthe period of time. Gratuity analytics component 820 may be furtherconfigured to determine a relative ranking for each merchant bycomparing the average ticket size of each merchant of the plurality ofmerchants, and generate the ticket size score for each merchant based onthe relative ranking.

In some implementations, gratuity calculation component 820 (or anyother component of gratuity analytics computing device 112) may befurther configured to calculate an average tip size for each merchantusing received transaction data 802 for a subset of the plurality ofmerchants. The average tip size represents an average amount of gratuityper transaction in each merchant during the period of time, and theaverage tip size may be calculated by averaging the difference betweenthe cleared transaction amount and the authorization transaction amountfor each transaction initiated in the merchant during the period oftime. Gratuity analytics component 830 may be further configured todetermine a relative ranking for each merchant by comparing the averagetip size of each merchant of the plurality of merchants, and generatethe tip size score for each merchant based on the relative ranking.

In some implementations, gratuity calculation component 820 (or anyother component of gratuity analytics computing device 112) may befurther configured to calculate competitor gratuity analytics usingreceived transaction data 802, POS information 804, and/or data andlocation data 806 for a subset of the plurality of merchants. Thecompetitor gratuity analytics represents the tip data collected for arestaurant's competitor (e.g., avg. tip size, avg. ticket size, etc).Gratuity analytics component 830 may be further configured to determinea relative ranking for each merchant by comparing the competitorgratuity analytics of each competitor and generate a competitor scorefor each merchant based on the relative ranking.

In some implementations, gratuity calculation component 820 (or anyother component of gratuity analytics computing device 112) may befurther configured to calculate a manager gratuity analytics usingreceived transaction data 802, POS information 804, data and locationdata 806 for a subset of the plurality of merchants. The managergratuity analytics represent the tip data collected for a manager ID.Gratuity analytics component 830 may be further configured to determinea relative ranking for each manager by comparing the manager gratuityanalytics of each manager, and generate a manager score for eachmerchant based on the relative ranking.

In some implementations, gratuity calculation component 820 (or anyother component of gratuity analytics computing device 112) may befurther configured to calculate employee gratuity analytics usingreceived transaction data 802, POS information 804, and/or data andlocation data 806 for a subset of the plurality of merchants. Theemployee gratuity analytics represents the tip data collected for anemployee ID (e.g., avg. tip size, avg. ticket size, etc). Gratuityanalytics component 830 may be further configured to determine arelative ranking for each employee by comparing the employee gratuityanalytics of each employee, and generate an employee score for eachemployee based on the relative ranking.

In some implementations, gratuity calculation component 820 (or anyother component of gratuity analytics computing device 112) may befurther configured to calculate inter-location gratuity analytics usingreceived transaction data 802, POS information 804, and/or data andlocation data 806 for a subset of the plurality of merchants. Theinter-location gratuity analytics represent the tip data collected for aplurality of the locations of restaurants. Gratuity analytics component830 may be further configured to determine a relative ranking for eachlocation of the restaurant by comparing the gratuity analytics of eachlocation of the restaurant, and generate a restaurant location score foreach restaurant based on the relative ranking. The inter-location scoreranks locations of the chain of restaurants compared to other locations.For example, it would compare restaurant locations in Missouri, Texas,and Florida for the same restaurant chain.

Gratuity analytics component 820 may be further configured to determinea relative ranking for each merchant by comparing the average tip sizeof each merchant of the plurality of merchants, and generate the tipsize score for each merchant based on the relative ranking.

In some implementations, Gratuity calculation component 820 may beconfigured to generate a merchant record for each merchant of theplurality of merchants. Receiving component 810 may be configured toreceive an investment goal associated with the plurality of merchants.Gratuity analytics component 840 may be configured to sort the pluralityof merchant records according to the investment goal and the gratuityanalytics for each sector in which each merchant of the plurality ofmerchants is located. Causing or displaying component 840 may beconfigured to present the sorted merchant records in an optimizedmerchant management portfolio.

FIGS. 9 and 10 are example screenshots of a user interface of a usercomputing device, including gratuity analytics generated by the gratuityanalytics computing device of FIG. 2. FIG. 9 is an example screenshot ofa user interface of a user computing device, including gratuityanalytics generated for a merchant (e.g., a restaurant chain). Anational dashboard view 900 includes a user 902, a dashboard guide 904,a date range 906, a merchant average tip 908, a competitors average tip910, a merchant average ticket size 912, a competitors average ticketsize 914, an average tip percentage 916, an average ticket sizepercentage 918, a national restaurant overview 920, and a top statesoverview 922. Dashboard guide 904 allows user 902 to navigate the userinterface. User 902 may select date range 906 to determine the daterange for the received transactional data. This data is used todetermine the national gratuity analytics for the restaurant chain. Thenational dashboard view 900 displays data for all restaurants of a chainnationally.

The transaction data is used to calculate the merchant average tip size908. The merchant average tip size 908 displays the national average tipsize for the merchant. A graphic (e.g., an arrow in the circle)indicates if average national tip size for the merchant has increased ordecreased within the date range 906. The competitors' average tip 910displays the national average tip size for the merchant's competitors. Agraphic (e.g., an arrow in the circle) indicates if the competitors'national average tip size is increasing or decreasing. This indicatesthe national performance of the merchant. In this example, the merchantaverage tip 908 is increasing while competitors average tip 910 isdecreasing indicating the merchant is improving, even while othersstruggle.

The merchant average ticket size 912 is the average ticket size for therestaurant nationally. The competitors' average ticket size 914 is theaverage ticket size for the restaurant's competitors nationally. Anaverage ticket size is the bill amount before the tip is added. In someembodiments, the ticket size may include the tip. In some instances, theaverage ticket size may be used to determine which restaurants arecompetitors of the restaurant. Average ticket size may indicate asimilarity in type of restaurant.

National dashboard view 900 includes an average tip percentage graphic916. Average tip percentage graph 916 displays the average tippercentage of the restaurant compared to the restaurant's competitors'average tip percentage within date range 906. The average tip percentageis the average of all national tip percentages at the restaurant. A tippercentage is the percent a customer tips on a ticket (or bill). Forexample, a twenty dollar tip on a hundred dollar ticket is a twentypercent tip. Using a percentage allows user 902 to compare merchantseven when the ticket size is not comparable. For example, when theticket sizes are different, the average tip size at the restaurant maybe lower but the average tip percentage may higher.

National dashboard view 900 includes an average ticket size graph 918.Average ticket size graph 918 compares the restaurant average ticketsize to the restaurant's competitors' average ticket size. The averageticket size is the average of all national tickets within the merchant.This offers a comparison between average ticket size of the restaurantand the restaurant's competitor. If competitors are offering a similarproduct to the restaurant this could indicate to the user 902 that theticket size may need to be altered. For example, if the average ticketsize of competitors is $9.00 and the average ticket size of therestaurant is $15.00 this may indicate that the restaurant has priceditems too high and a decrease in item prices could improve sales.

National restaurant overview 920 displays the geographical location ofrestaurants nationally. Top states overview 922 displays the topnational locations graphically. For example, states with the highestaverage tip size, highest total sales, etc., are displayed graphically.In the example embodiment, the top five states are displayed (otherembodiments may include a different number of top states). Top statesoverview 922 allows for user 902 interaction. This interaction may allowuser 902 to determine the ranking of the states, to view the sales andtip data of each state, the location of the restaurants in each stateand other relevant analytics.

National dashboard view 900 allows user 902 to compare the restaurantchain to the chain's competitors to determine which areas of the nationare thriving and which are failing. Further, user 902 may compare tipdata to which restaurants are providing acceptable and above acceptableservice and which restaurants are benefiting from a strong localeconomy.

FIG. 10 is an example screenshot of a user interface of a user computingdevice, including gratuity analytics generated for a restaurant chain. Astate dashboard view 1000 includes a merchant state average tip size1008, a competitors state average tip 1010, a merchant state averageticket size 1012, a competitors state average ticket size 1014, anaverage state tip percentage 1016, an average state ticket size 1018, astate restaurant overview 1020, and a city restaurant overview 1022.User 902 may select date range 906 to determine the date range for thereceived transactional data. This data determines state by stategratuity analytics for the restaurant chain. State dashboard view 1000displays data for all restaurants of a chain nationally.

The transactional data determines merchant state average tip size 1008.Merchant average state tip size 1008 displays the state average tip sizefor the merchant. A graphic (an arrow in the circle) indicates if theaverage tip for the merchant has increased or decreased within daterange 906. Competitors state average tip 1010 displays the state averagetip size for the merchant's competitors. A graphic (an arrow in thecircle) indicates if the competitors' average tip size is increasing ordecreasing. This indicates performance of the merchant within the state.In this example, merchant state average tip 1008 is increasing whilecompetitors state average tip 1010 is decreasing indicating the merchantis improving even while others struggle.

Merchant state average ticket size 1012 is the average ticket size forthe restaurant within the state. Competitors state average ticket size1014 is the average ticket size for the restaurant's competitors withinthe state. In some instances, the average ticket size may be used todetermine which restaurants are competitors of the restaurant. Averageticket size may indicate a similarity in type of restaurant.

State dashboard view 1000 includes an average state tip percentage graph1016. Average state tip percentage graph 1016 displays the restaurant'sstate average tip percentage of the restaurant compared to therestaurant's competitors' state average tip percentage within date range906. The average tip percentage is the average of all state tippercentages at the restaurant within the state. A tip percentage is thepercent a customer tips on a ticket (or bill). For example, a twentydollar tip on a hundred dollar ticket is a twenty percent tip. Using apercentage allows user 902 to compare merchants even when the ticketsize is not comparable. For example, when the ticket sizes are differentthe average tip size at the restaurant may be lower but the average tippercentage may higher.

State dashboard view 1000 includes an average state ticket size graph1018. The average state ticket size graph 1018 compares the restaurantaverage ticket size to the restaurant's competitors' average ticketsize. The average ticket size is the average of all state tickets withinthe merchant. This offers a comparison between average ticket size ofthe restaurant and the competitor. If a competitor is offering a similarproduct to the restaurant this could indicate to user 902 that theticket size may need to be altered.

State restaurant overview 1020 displays the location of restaurantswithin the state. Top cities overview 1022 displays the top citylocations within the state geographically. For example, the cities withthe highest average tip size are considered top cities. User 902 maychoose what determines the top cities (e.g., average tip percentage,total sales, etc.). In the example embodiment, the top five cities aredisplayed. This interaction may allow user 902 to determine the rankingof the states, to view the sales and tip data of each state, thelocation of the restaurants in each state and other relevant analytics.

State dashboard view 1000 allows user 902 to compare restaurants of achain within a state to determine which restaurants are thriving andwhich are failing. State dashboard view 1000 further allows user 902 tocompare the restaurant chain to the chain's competitor's within thestate. This indicates to the user 902 if the restaurant chain has a highperformance in comparison to chains in the market.

In one embodiment, a method for generating real-time gratuity analyticsfor one or more restaurants is described. The method is implemented by agratuity analytics computing device that includes at least one processorin communication with a memory. The method includes receiving areal-time point-of-sale (POS) data feed from a POS device associatedwith a restaurant; receiving, as part of the POS data feed, anauthorization transaction initiated by a cardholder consumer within therestaurant, the authorization transaction including a first bill amount,an employee identifier, a manager identifier, and a table identifier;receiving, as part of the POS data feed, a clearing transaction matchedto the authorization transaction, the clearing transaction including asecond bill amount; calculating a tip size based on the authorizationtransaction and the clearing transaction; comparing the tip size to athreshold tip size range; and when the tip size falls outside of thethreshold tip size range, transmitting an alert to a device associatedwith the manager identifier.

The method further includes generating the threshold tip size rangebased on historical transaction data for the restaurant.

The method further includes formatting the alert and transmitting thealert as at least one of a short message service message, an e-mail, andan application notification.

The method further includes accessing the POS device using anapplication programming interface.

The method further includes transmitting the alert to a deviceassociated with the manager identifier, wherein the alert includes theemployee identifier and the table identifier associated with theauthorization transaction.

In another embodiment, a computer-readable storage medium havingcomputer-executable instructions embodied thereon, wherein when executedby a gratuity analytics computing device having at least one processorin communication with a memory, the computer-readable instructions causethe gratuity analytics computing device to: receive a real-timepoint-of-sale (POS) data feed from a POS device associated with arestaurant; receive, as part of the POS data feed, an authorizationtransaction initiated by a cardholder consumer within the restaurant,the authorization transaction including a first bill amount, an employeeidentifier, a manager identifier, and a table identifier; receive, aspart of the POS data feed, a clearing transaction matched to theauthorization transaction, the clearing transaction including a secondbill amount; calculate a tip size using based on the authorizationtransaction and the clearing transaction; compare the tip size to athreshold tip size range; and when the tip size falls outside of thethreshold tip size range, transmit an alert to a device associated withthe manager identifier.

As used herein, the term “non-transitory computer-readable media” isintended to be representative of any tangible computer-based deviceimplemented in any method or technology for short-term and long-termstorage of information, such as, computer-readable instructions, datastructures, program modules and sub-modules, or other data in anydevice. Therefore, the methods described herein may be encoded asexecutable instructions embodied in a tangible, non-transitory, computerreadable medium, including, without limitation, a storage device and/ora memory device. Such instructions, when executed by a processor, causethe processor to perform at least a portion of the methods describedherein. Moreover, as used herein, the term “non-transitorycomputer-readable media” includes all tangible, computer-readable media,including, without limitation, non-transitory computer storage devices,including, without limitation, volatile and nonvolatile media, andremovable and non-removable media such as a firmware, physical andvirtual storage, CD-ROMs, DVDs, and any other digital source such as anetwork or the Internet, as well as yet to be developed digital means,with the sole exception being a transitory, propagating signal.

This written description uses examples to disclose the disclosure,including the best mode, and also to enable any person skilled in theart to practice the embodiments, including making and using any devicesor systems and performing any incorporated methods. The patentable scopeof the disclosure is defined by the claims, and may include otherexamples that occur to those skilled in the art. Such other examples areintended to be within the scope of the claims if they have structuralelements that do not differ from the literal language of the claims, orif they include equivalent structural elements with insubstantialdifferences from the literal languages of the claims.

What is claimed is:
 1. A gratuity analytics computing device forgenerating gratuity analytics for one or more restaurants, said gratuityanalytics computing device comprising a memory in communication with aprocessor, said processor programmed to: receive a date range from aclient computing device; receive transaction data for transactionsoccurring within the date range at a restaurant, the transaction dataincluding a manager identifier, a time stamp, and an employee identifierassociated with the transactions, the transaction data includingauthorization messages and clearing messages; match a plurality ofauthorization messages with a respective plurality of clearing messages;calculate tip data for the restaurant based on the plurality of matchedmessages; generate gratuity analytics for the restaurant over the daterange based on the tip data; and display on a user interface of theclient computing device the gratuity analytics.
 2. The gratuityanalytics computing device of claim 1, wherein the gratuity analyticsinclude a comparison of tip data between different time intervals withinthe date range.
 3. The gratuity analytics computing device of claim 1,wherein said processor is further programmed to: determine a pluralityof subsets of the plurality of matched messages, each subset associatedwith a respective manager identifier; for each subset, calculate anaverage tip size; and compare the average tip size between the subsetsto generate manager gratuity analytics.
 4. The gratuity analyticscomputing device of claim 1, wherein said processor is furtherprogrammed to: determine a plurality of subsets of the plurality ofmatched messages, each subset associated with a respective employeeidentifier; for each subset, calculate an average tip size; and comparethe average tip size between subsets to generate employee gratuityanalytics.
 5. The gratuity analytics computing device of claim 1,wherein the restaurant is a first restaurant and the tip data is firsttip data, and wherein said processor is further programmed to: calculatesecond tip data for a second restaurant; and compare the first tip datato the second tip data to generate inter-location gratuity analytics. 6.The gratuity analytics computing device of claim 1, wherein saidprocessor is further programmed to: identify a competitor restaurant ofthe restaurant; retrieve transaction data for the competitor restaurant;calculate, using the transaction data for the competitor restaurant,competitor tip data for the competitor restaurant; and compare the tipdata for the restaurant to the competitor tip data to generatecompetitor gratuity analytics.
 7. The gratuity analytics computingdevice of claim 6, wherein to identify the competitor restaurant, saidprocessor is further programmed to: calculate, using the transactiondata for the restaurant, an average ticket size and sales volume for therestaurant; retrieve transaction data for a plurality of potentialcompetitor restaurants; calculate, using the transaction data for theplurality of potential competitor restaurants, a respective averageticket size and sales volume for each of the plurality of potentialcompetitor restaurants; and identify the competitor restaurant as apotential competitor restaurant of the plurality of potential competitorrestaurants having an average ticket size and sales volume most similarto the restaurant.
 8. The gratuity analytics computing device of claim1, wherein the transaction data further includes a card type of apayment card used to initiate each respective transaction, and whereinsaid processor is further programmed to: determine at least one commonlyused card type; identify an issuer associated with each at least onecommonly used card type; and transmit an issuer identifier for eachidentified issuer to the restaurant.
 9. A method for generating gratuityanalytics for one or more restaurants, said method implemented by agratuity analytics computing device including at least one processor incommunication with a memory, the gratuity analytics computing device incommunication with a client computing device, said method comprising:receiving a date range from a client computing device; receivingtransaction data for transactions occurring within the date range at arestaurant, the transaction data including a manager identifier, timestamp, and an employee identifier associated with the transactions, thetransaction data including data from authorization messages and clearingmessages; matching a plurality of authorization messages with arespective plurality of clearing messages; calculating tip data for therestaurant based on the plurality of matched messages; generatinggratuity analytics for the restaurant over the date range based on thetip data; and displaying on a user interface of the client computingdevice the gratuity analytics.
 10. The method of claim 9 furthercomprising comparing the tip data between different time intervalswithin the date range.
 11. The method of claim 9 further comprising:determining a plurality of subsets of the plurality of matched messages,each subset associated with a respective manager identifier; for eachsubset, calculating an average tip size; and comparing the average tipsize between subsets to generate manager gratuity analytics.
 12. Themethod of claim 9 further comprising: determining a plurality of subsetsof the plurality of matched messages associated with a respectiveemployee identifier; for each subset, calculating an average tip size;and comparing the average tip size between the subsets to generateemployee gratuity analytics.
 13. The method of claim 9, wherein therestaurant is a first restaurant and the tip data is first tip data, andwherein said method further comprises: calculating second tip data for asecond restaurant; and comparing the first tip data to the second tipdata to generate inter-location gratuity analytics.
 14. The method ofclaim 13 further comprising: identifying a competitor restaurant of therestaurant; retrieving transaction data for the competitor restaurant;calculating, using the transaction data for the competitor restaurant,competitor tip data for the competitor restaurant; and comparing the tipdata for the restaurant to the competitor tip data to generatecompetitor gratuity analytics.
 15. The method of claim 13 furthercomprising: calculating, using the transaction data for the restaurant,an average ticket size and sales volume for the restaurant; retrievingtransaction data for a plurality of potential competitor restaurants;calculating, using the transaction data for the plurality of potentialcompetitor restaurants, a respective average ticket size and salesvolume for each of the plurality of potential competitor restaurants;and identifying the competitor restaurant as a potential competitorrestaurant of the plurality of potential competitor restaurants havingan average ticket size and sales volume most similar to the restaurant.16. The method of claim 9, wherein the transaction data further includesa card type of a payment card used to initiate each of the respectivetransactions, and wherein said method further comprises: determining atleast one commonly used card type; identifying an issuer associated witheach at least one commonly used card type; and transmitting an issueridentifier for each identified issuer to the restaurant.
 17. Acomputer-readable storage medium having computer-executable instructionsembodied thereon, wherein when executed by a gratuity analyticscomputing device including at least one processor in communication witha memory, the computer-readable instructions cause the gratuityanalytics computing device to: receive a date range from a clientcomputing device; receive transaction data for transactions occurringwithin the date range at a restaurant, the transaction data including amanager identifier, time stamp, and an employee identifier associatedwith the transactions, the transaction data included withinauthorization messages and clearing messages; match a plurality ofauthorization messages with a respective plurality of clearing messages;calculate tip data for the restaurant, the tip data on the plurality ofmatched messages; generate gratuity analytics for the restaurant overthe date range based on the tip data; and display on a user interface ofthe client computing device the gratuity analytics.
 18. Thecomputer-readable storage medium of claim 17, wherein thecomputer-executable instructions further cause the gratuity analyticscomputing device to: determine a plurality of subsets of the pluralityof matched messages, each subset associated with a respective manageridentifier; for each respective subset, calculate an average tip size;and compare the average tip size between the subsets to generate managergratuity analytics.
 19. The computer-readable storage medium of claim17, wherein the computer-executable instructions further cause thegratuity analytics computing device to: determine a plurality of subsetsof the plurality of matched messages associated with a respectiveemployee identifier; for each subset, calculate an average tip size; andcompare the average tip size between the subsets to generate employeegratuity analytics.
 20. The computer-readable storage medium of claim17, wherein the computer-executable instructions further cause thegratuity analytics computing device to: calculate second tip data for asecond restaurant; and compare the first tip data to the second tip datato generate inter-location gratuity analytics.
 21. The computer-readablestorage medium of claim 17, wherein the computer-executable instructionsfurther cause the gratuity analytics computing device to: identify acompetitor restaurant of the restaurant; retrieve transaction data forthe competitor restaurant; calculate, using the transaction data for thecompetitor restaurant, competitor tip data for the competitorrestaurant; and compare the tip data for the restaurant to thecompetitor tip data to generate competitor gratuity analytics.
 22. Agratuity analytics computing device for generating real-time gratuityanalytics for one or more restaurants, said gratuity analytics computingdevice comprising a memory in communication with a processor, saidprocessor programmed to: receive a real-time point-of-sale (POS) datafeed from a POS device associated with a restaurant; receive, as part ofthe POS data feed, an authorization message for a transaction initiatedby a cardholder consumer within the restaurant, the authorizationmessage including a first bill amount, an employee identifier, a manageridentifier, and a table identifier; receive, as part of the POS datafeed, a clearing message for matching with the authorization message forthe transaction, the clearing transaction including a second billamount; calculate a tip size based on the authorization message and theclearing message; compare the tip size to a threshold tip size range;when the tip size falls outside of the threshold tip size range,transmit an alert to a device associated with the manager identifier.23. The gratuity analytics computing device of claim 22, wherein saidprocessor is further programmed to generate the threshold tip size rangebased on historical transaction data for the restaurant.
 24. Thegratuity analytics computing device of claim 22, wherein said processoris programmed to format the alert and transmit the alert as at least oneof a short message service message, an e-mail, and an applicationnotification, wherein the alert is configured to cause the device todisplay the alert.
 25. The gratuity analytics computing device of claim22, wherein said processor is programmed to access the POS device usingan application programming interface.
 26. The gratuity analyticscomputing device of claim 22, wherein said processor is programmed totransmit the alert to a device associated with the manager identifier,the alert including the employee identifier and the table identifierassociated with the authorization transaction.